Vulnerability = Exposure + Sensitivity + Adaptive Capacity
IPCC Climate Change 2001: Impacts, Adaptation, and Vulnerability (eds McCarthy, J. J., Canziani, O. F., Leary, N. A., Dokken, D. J. & White, K. S.)

Exposure is the immediate effect

Sensitivity represents the longstanding innate properties of the population

Adaptive capacity are the things that can be done to change impacts

We aim to create a vulnerability index for the Brisbane City local government area (LGA) predicting the vulnerability of each level 2 statistical area (SA2).

Exposure: flood extent intersection with properties, services, infrastructure

# simple features package for vector
# spatial data
library(sf)
# for data manipulation
library(tidyverse)
# Australian maps
library(ozmaps)

Code is courtesy of Rich Cotrell

Great data from Queensland Spatial Catalogue - QSpatial

flood_extents_shp <- st_read(paste0(path,
  "spatial/Flood__Awareness__Extents/Flood___Awareness___Extents.shp"))

ggplot() + geom_sf(data = flood_extents_shp,
  fill = "lightblue", colour = "grey90",
  size = 0.01) + theme_bw()

lgas <- ozmaps::ozmap_data(data = "abs_lga")

bris_lga <- lgas %>%
  filter(NAME == unique(lgas$NAME)[grep("Brisbane",
    unique(lgas$NAME))]) %>%
  st_transform(crs = st_crs(flood_extents_shp))

From the Australian Bureau of Statistics:

SA2_aus <- st_read(paste0(path, "spatial/sa2_2016_aust_shape/SA2_2016_AUST.shp"))

SA2_bris <- SA2_aus %>%
  filter(STE_NAME16 == "Queensland" &
    GCC_NAME16 == "Greater Brisbane" &
    SA4_NAME16 %in% c("Brisbane - East",
      "Brisbane - North", "Brisbane - South",
      "Brisbane - West", "Brisbane Inner City") &
    (!SA2_NAME16 %in% c("Redland Islands"))) %>%
  st_transform(crs = st_crs(flood_extents_shp))

ggplot() + geom_sf(data = bris_lga) + labs(title = "LGA") +
  theme(plot.title = element_text(size = 30)) +
  theme_bw()
ggplot() + geom_sf(data = SA2_bris) + labs(title = "SA2s") +
  theme(plot.title = element_text(size = 30)) +
  theme_bw()

ems <- st_read(paste0(path, "/spatial/QSC_QLD emergency service locations/Emergency_services_facilities.shp")) %>%
  st_transform(crs = st_crs(flood_extents_shp))

ems_crop <- st_crop(x = ems, y = st_bbox(flood_extents_shp)) %>%
  filter(ATTRIBUTES %in% c("Queensland Ambulance Service",
    "Queensland Fire and Emergency Services",
    "Queensland Police Service"))

ems_intersect <- st_intersection(ems_crop,
  SA2_bris)

ggplot() + geom_sf(data = SA2_bris, alpha = 0,
  size = 0.5) + geom_sf(data = ems_intersect,
  aes(colour = ATTRIBUTES), size = 1) +
  theme_bw()

petrol <- st_read(paste0(path, "spatial/Petrol_Stations_Australia/PetrolStations.gdb")) %>%
  st_transform(crs = st_crs(flood_extents_shp))

petrol_crop <- st_crop(x = petrol, y = st_bbox(SA2_bris))
petrol_intersect <- st_intersection(x = petrol_crop,
  y = SA2_bris)

How to turn this in to an index?

• Combine all the variables of each component (Exposure etc) into a score
• Add exposure, sensitivity, adaptive capacity and scale

Example: SpatialEpiApp for disease surveillance by Paula Moraga.

SpatialEpiApp is a Shiny web application that allows to visualize spatial and spatio-temporal disease data, estimate disease risk, and detect clusters.